Visualizing High-Resolution Grids

In this tutorial, you’ll learn about:

• Techniques for visualuzing higher-resolution grids

Related Documentation

• UXarray Plotting User Guide

Time to learn: 15 minutes

---

Introduction

Up to this point in the cookbook, we have only used coarse-resolution unstructured grids.

import cartopy.crs as ccrs
import uxarray as ux

grid_path = "../../meshfiles/x1.655362.grid.nc"
data_path = "../../meshfiles/x1.655362.data.nc"

uxds = ux.open_dataset(grid_path, data_path)
uxds["relhum_200hPa"]

<xarray.UxDataArray 'relhum_200hPa' (Time: 1, n_face: 655362)> Size: 3MB
[655362 values with dtype=float32]
Dimensions without coordinates: Time, n_face
Attributes:
    units:      percent
    long_name:  Relative humidity vertically interpolated to 200 hPa

<xarray.UxDataArray 'relhum_200hPa' (Time: 1, n_face: 655362)> Size: 3MB
[655362 values with dtype=float32]
Dimensions without coordinates: Time, n_face
Attributes:
    units:      percent
    long_name:  Relative humidity vertically interpolated to 200 hPa

uxarray.UxDataArray

'relhum_200hPa'

• Time: 1
• n_face: 655362

• ...

  [655362 values with dtype=float32]

• Coordinates: (0)
• Indexes: (0)
• Attributes: (2)

  units :

  percent

  long_name :

  Relative humidity vertically interpolated to 200 hPa

Show Grid Information

<uxarray.Grid>
Original Grid Type: MPAS
Grid Dimensions:
  * n_node: 1310720
  * n_face: 655362
  * n_max_face_nodes: 10
  * n_max_node_faces: 3
  * n_nodes_per_face: (655362,)
Grid Coordinates (Spherical):
  * node_lon: (1310720,)
  * node_lat: (1310720,)
  * face_lon: (655362,)
  * face_lat: (655362,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
  * face_node_connectivity: (655362, 10)
  * node_face_connectivity: (1310720, 3)
Grid Descriptor Variables:
  * n_nodes_per_face: (655362,)

uxarray.UxDataArray.uxgrid

• Dimensions:
  • n_node: 1310720
  • n_face: 655362
  • n_max_face_nodes: 10
  • n_max_node_faces: 3
• Spherical Coordinates: (4)
  • node_lon
    (n_node)
    float32
    -175.0 -174.8 ... -103.0 -175.0

    standard_name :

    longitude

    long name :

    Longitude of the corner nodes of each face

    units :

    degrees_east

    array([-174.95294, -174.81796, -174.86942, ...,  113.04705, -102.95294,
           -174.95294], dtype=float32)

  • node_lat
    (n_node)
    float32
    26.44 26.53 26.67 ... -52.62 -52.62

    standard_name :

    latitude

    long name :

    Latitude of the corner nodes of each face

    units :

    degrees_north

    array([ 26.43807 ,  26.525747,  26.667755, ..., -10.812317, -52.62263 ,
           -52.62263 ], dtype=float32)

  • face_lon
    (n_face)
    float32
    -175.0 -103.0 ... -175.2 -174.7

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([-174.95294, -102.95294,  115.01709, ..., -174.95294, -175.1781 ,
           -174.72784], dtype=float32)

  • face_lat
    (n_face)
    float32
    26.57 26.57 90.0 ... -52.54 -52.54

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([ 26.56505 ,  26.56505 ,  90.      , ..., -52.780724, -52.54337 ,
           -52.54337 ], dtype=float32)

• Cartesian Coordinates: (0)
• Connectivity: (2)
  • node_face_connectivity
    (n_node, n_max_node_faces)
    int64
    0 163843 163844 ... 655360 655361

    cf_role :

    node_face_connectivity

    long name :

    Faces that neighbor each node

    start_index :

    0

    _FillValue :

    -9223372036854775808

    array([[     0, 163843, 163844],
           [     0, 163844, 163845],
           [     0, 163845, 163846],
           ...,
           [655353, 655354, 655355],
           [655356, 655357, 655358],
           [655359, 655360, 655361]])

  • face_node_connectivity
    (n_face, n_max_face_nodes)
    int64
    0 1 ... -9223372036854775808

    cf_role :

    face_node_connectivity

    long name :

    Maps every face to its corner nodes.

    start_index :

    0

    _FillValue :

    -9223372036854775808

    array([[                   0,                    1,                    2,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [                   5,                    6,                    7,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [                  10,                   11,                   12,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           ...,
           [              983022,              1228798,               983034,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [             1310719,               983028,               983033,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808],
           [             1310719,               983039,               983038,
            ..., -9223372036854775808, -9223372036854775808,
            -9223372036854775808]])

• Descriptors: (0)
• Attributes: (54)

  model_name :

  mpas

  core_name :

  init_atmosphere

  source :

  MPAS

  Conventions :

  MPAS

  git_version :

  v6.0-dirty

  on_a_sphere :

  YES

  sphere_radius :

  6371229.0

  is_periodic :

  NO

  x_period :

  0.0

  y_period :

  0.0

  history :

  Mon Aug 16 12:09:52 2021: ncks -v latVertex,lonVertex,latCell,lonCell,latEdge,lonEdge,cellsOnVertex,verticesOnCell,verticesOnEdge,edgesOnVertex,edgesOnCell,nEdgesOnCell,angleEdge x1.655362.init.nc foo.nc mpirun -n 18 ./init_atmosphere_model

  parent_id :

  0hhmjs26t0

  mesh_spec :

  0.0

  config_init_case :

  7

  config_calendar_type :

  gregorian

  config_start_time :

  2016-08-01_00:00:00

  config_stop_time :

  2016-09-10_00:00:00

  config_theta_adv_order :

  3

  config_coef_3rd_order :

  0.25

  config_num_halos :

  2

  config_nvertlevels :

  75

  config_nsoillevels :

  4

  config_nfglevels :

  38

  config_nfgsoillevels :

  4

  config_months :

  12

  config_geog_data_path :

  /glade/p/work/wrfhelp/WPS_GEOG/

  config_met_prefix :

  FILE

  config_sfc_prefix :

  SST

  config_fg_interval :

  86400

  config_landuse_data :

  USGS

  config_topo_data :

  GMTED2010

  config_use_spechumd :

  NO

  config_ztop :

  40000.0

  config_nsmterrain :

  1

  config_smooth_surfaces :

  YES

  config_dzmin :

  0.3

  config_nsm :

  30

  config_tc_vertical_grid :

  YES

  config_extrap_airtemp :

  linear

  config_static_interp :

  NO

  config_native_gwd_static :

  NO

  config_gwd_cell_scaling :

  1.0

  config_vertical_grid :

  YES

  config_met_interp :

  YES

  config_input_sst :

  NO

  config_frac_seaice :

  YES

  config_pio_num_iotasks :

  0

  config_pio_stride :

  1

  config_block_decomp_file_prefix :

  x1.655362.graph.info.part.

  config_number_of_blocks :

  0

  config_explicit_proc_decomp :

  NO

  config_proc_decomp_file_prefix :

  graph.info.part.

  file_id :

  ytj0fh0qmd

  NCO :

  netCDF Operators version 4.9.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

Polygons

As was mentioned in the Data Visualization notebook, it is suggested to keep rasterize=True for higher-resolution grids, especially at a global scales.

%%time
uxds["relhum_200hPa"][0].plot.polygons(rasterize=True)

CPU times: user 2.81 s, sys: 290 ms, total: 3.1 s
Wall time: 3.12 s

The initial execution time for polygon plotting routines will always be significantly longer since each face is converted to a Polygon and stored as a GeoDataFrame. Once this initial processing is done, the geometries can be re-used. Any subsequent polygon plots will render significantly faster.

%%time
res = uxds["relhum_200hPa"][0].plot.polygons(rasterize=True)

CPU times: user 310 ms, sys: 16.5 ms, total: 326 ms
Wall time: 343 ms

It is often a good idea to pair polygon plotting with the subset functionality, especially if you are only interested in visualuzing a specific region. Subsetting will reduce the total number of polygons that are required to be render.

relhum_subset = uxds["relhum_200hPa"][0].subset.bounding_box(
    lon_bounds=[-5, 5], lat_bounds=[-2.5, 2.5]
)
relhum_subset.plot.polygons(rasterize=True)

Points

At higher resolutions,

%%time
uxds["relhum_200hPa"][0].plot.points(rasterize=True)

CPU times: user 15.4 ms, sys: 3.33 ms, total: 18.7 ms
Wall time: 17.7 ms

%%time
uxds["relhum_200hPa"][0].plot.points(rasterize=True, projection=ccrs.Orthographic())

CPU times: user 18.1 ms, sys: 2.48 ms, total: 20.6 ms
Wall time: 19.9 ms

uxds["relhum_200hPa"][0].plot.points(
    rasterize=True, projection=ccrs.Orthographic(), pixel_ratio=1.5, dynamic=False
)

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